⚠️ Scope & Medical Disclaimer
This repository represents an early-stage framework for clinical knowledge engineering.
It is:
- Not a diagnostic system
- Not a treatment recommendation system
- Not a complete medical knowledge base
All outputs are structured representations of clinical patterns, not medical advice.
This framework is intended for research, system design, and community-driven knowledge structuring.
What PeachBot Medical KG
PeachBot Medical KG is a framework for building structured, explainable clinical knowledge systems.
It provides:
- A schema for representing clinical data
- A rule-based structure for encoding patterns
- A pipeline to convert knowledge into machine-readable JSON
Think of it as:
A foundation layer for building deterministic, explainable medical knowledge systems.
Why This Exists
Many AI systems in healthcare focus on prediction:
input → model → prediction
But this approach often lacks:
- Transparency
- Auditability
- Explicit reasoning structure
This project explores a different direction:
How to explicitly encode clinical reasoning in a structured, verifiable way.
Core Idea
Instead of predicting outcomes, the framework enables:
Clinical Patterns → Structured Rules → Knowledge Outputs (JSON)
Where:
- Inputs = symptoms, signs, investigations
- Output = structured knowledge objects
- Logic = deterministic and explainable
What This Framework Provides
- A structured schema for clinical knowledge
- Rule-based pattern encoding
- Deterministic knowledge generation
- JSON export pipeline
- Explainability by design
What This Framework Does NOT Provide
- ❌ No diagnosis
- ❌ No treatment recommendation
- ❌ No machine learning
- ❌ No real-time patient decision system
- ❌ No complete medical dataset
Role in the PeachBot System
peachbot-medical-kg → defines structured knowledge
PeachBot Core → consumes and reasons over it
This separation allows:
- Clean system boundaries
- Independent evolution of knowledge and execution
- Better validation and auditing
Architecture Overview
The framework is organized into:
- Schema Layer → defines clinical data structure
- Rules Layer → encodes patterns
- Builders → utilities to construct rules
- Engine → transforms rules into outputs
- Exporters → generates structured JSON
- Outputs → compiled knowledge artifacts
Repository Structure
schema/ → clinical data models
rules/ → pattern definitions
builders/ → rule construction tools
engine/ → transformation logic
exporters/ → JSON generation
outputs/ → generated knowledge
scripts/ → execution entry points
How to Use This Framework
1. Define Clinical Patterns
- Identify structured inputs (e.g., symptoms, signals)
2. Encode as Rules
- Use rule definitions to represent relationships
3. Apply Schema
- Ensure consistency and structure
4. Generate Knowledge
python scripts/export_medai.py
What Currently Exists
- Base schema and structure
- Rule definition system
- Knowledge generation pipeline
- Initial example outputs
What Is Still Missing
- Large-scale curated knowledge
- Extensive clinical coverage
- Validation datasets
- Community contributions
Design Philosophy
This framework is built on:
- Deterministic logic (no black-box systems)
- Explainability by default
- Structured, auditable knowledge
- Separation of knowledge and execution
Where This Can Be Used (Exploratory)
- Clinical knowledge structuring
- Explainable AI pipelines
- Edge-based healthcare systems
- Research in medical reasoning systems
(Not for clinical deployment)
🤝 Contributing
This framework is designed to be community-extendable.
You Can Contribute By:
- Adding clinical pattern rules
- Improving schema design
- Enhancing explainability
- Structuring domain-specific knowledge
Contribution Rules
All contributions must:
- Be pattern-based (not diagnostic claims)
- Include clear explanations
- Prefer evidence-backed reasoning
- Maintain deterministic logic
Do NOT Contribute
- Diagnosis statements
- Treatment recommendations
- Unverified medical claims
- Black-box logic
Repository
👉 https://github.com/peachbotAI/peachbot-medical-kg
Final Note
This is not a finished system.
It is a framework for building structured clinical knowledge systems.
The goal is to enable:
A shared, transparent, and explainable approach to representing clinical reasoning.
Citation
Swapin Vidya. PeachBot Medical Knowledge Graph (Framework), 2026
